The Strength of Gradually Accruing Probabilistic Evidence Modulates Brain Activity during a Categorical Decision

Abstract

The evolution of neural activity during a perceptual decision is well characterized by the evidence parameter in sequential sampling models. However, it is not known whether accumulating signals in human neuroimaging are related to the integration of evidence. Our aim was to determine whether activity accumulates in a non-perceptual task by identifying brain regions tracking the strength of probabilistic evidence. Functional magnetic resonance imaging was used to measure whole-brain activity as choices were informed by integrating a series of learned prior probabilities. Subjects first learned the predictive relationship between a set of shape stimuli and one of two choices. During scanned testing, they made binary choices informed by the sum of the predictive strengths of individual shapes. Sequences of shapes adhered to three distinct rates of evidence (RoE), rapid, gradual, and switch. We predicted that activity in regions informing the decision would modulate as a function of RoE prior to the choice. Activity in some regions, including premotor areas, changed as a function of RoE and response hand, indicating a role in forming an intention to respond. Regions in occipital, temporal, and parietal lobes modulated as a function of RoE only, suggesting a pre-response stage of evidence processing. In all of these regions, activity was greatest on rapid trials and least on switch trials, which is consistent with an accumulation-to-boundary account. In contrast, activity in a set of frontal and parietal regions was greatest on switch and least on rapid trials, which is consistent with an effort or time-on-task account.

Evidence accumulation detected in BOLD signal using slow perceptual decision making

“…A potential problem with these previous studies of delaying decisions by gradually revealing a stimulus is that manipulating stimulus evidence is likely to affect a different level of cognitive processing (O'Connell, Dockree, & Kelly, 2012) while confounding perceptual detection with evidence accumulation. Thus, this type of manipulation may not selectively identify the neural integration process (Wheeler et al, 2015). Nevertheless, our observation of accumulator-like activity in the bilateral anterior insula is consistent with these previous studies.…”

“…This approach may suffice in controlling for the perception/accumulation confound, though it precludes the use of simple, continuous stimuli. Tremel and Wheeler (2015) maintained a constant input of degraded images in a face/house recognition task, but RTs were not sufficiently long (around 3 seconds) to measure ramping activity. Also, it is possible that face or object recognition is a nonlinear process and that revealing degraded images or masked local parts would interact with the accumulation process in a temporally unstable way.…”

“…The neural correlates of perceptual decision making have been studied extensively using monkey neurophysiology (e.g., Gold & Shadlen, 2000) and human electroencephalography (EEG; e.g., van Vugt, Simen, Nystrom, Holmes & Cohen, 2012), magnetoencephalography (MEG; e.g., Donner, Siegel, Fries, & Engel, 2009), and functional magnetic resonance imaging (fMRI; e.g., H. R. Heekeren, Marrett, Ruff, Bandettini, & Ungerleider, 2006;Wheeler et al, 2015;see Mulder, Van Maanen, & Forstmann, 2014, for a meta analysis). In comparison to the other methods, the temporal resolution of fMRI (on the order of 2 seconds) is very slow-too slow, in fact, to capture the sub-second time course of most perceptual decisions.…”

Brain Mechanisms of Concept Learning

“…Here we focus on the vmPFC and an additional area, the intraparietal sulcus (IPS)/inferior parietal (IP), both of which are associated with both reward and concept representation. IPS/IP is active during categorization and is sensitive to category representation in humans (Seger et al, 2015;Wheeler et al, 2015) and nonhuman primates (Sarma et al, 2016). Braunlich and Seger (2016;K.…”

Modeling Cognitive Processes with Neural Reinforcement Learning

“…Work with patients showed that performance on the weather prediction task is primarily dependent on the basal ganglia, and not as much on medial temporal lobe learning system [Knowlton et al, 1994, Knowlton et al, 1996. Later neuroimaging work confirmed the contribution of the basal ganglia to probabilistic category learning [Shohamy et al, 2004, Poldrack et al, 2001, Ashby and Maddox, 2005, Shohamy et al, 2008, Wheeler et al, 2015, Soltani et al, 2016. The computational framework of reinforcement learning (RL) has been instrumental in explaining the involvement of the basal ganglia in trial-and-error learning: by learning the future reward value of observations (or states) using prediction errors that are conveyed by midbrain dopaminergic neurons , Montague et al, 1996, Niv, 2009, Schultz et al, 1997.…”